A Comparison of Feature Detectors with Passive and Task-Based Visual Saliency

  • Patrick Harding
  • Neil M. Robertson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)


This paper investigates the coincidence between six interest point detection methods (SIFT, MSER, Harris-Laplace, SURF, FAST & Kadir-Brady Saliency) with two robust “bottom-up” models of visual saliency (Itti and Harel) as well as “task” salient surfaces derived from observer eye-tracking data. Comprehensive statistics for all detectors vs. saliency models are presented in the presence and absence of a visual search task. It is found that SURF interest-points generate the highest coincidence with saliency and the overlap is superior by 15% for the SURF detector compared to other features. The overlap of image features with task saliency is found to be also distributed towards the salient regions. However the introduction of a specific search task creates high ambiguity in knowing how attention is shifted. It is found that the Kadir-Brady interest point is more resilient to this shift but is the least coincident overall.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Patrick Harding
    • 1
    • 2
  • Neil M. Robertson
    • 1
  1. 1.School of Engineering and Physical SciencesHeriot-Watt Univ.UK
  2. 2.Thales Optronics Ltd.UK

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